Synthesis‐Style Auto‐Correlation‐Based Transformer: A Learner on Ionospheric TEC Series Forecasting
Abstract Accurate 1‐day global total electron content (TEC) forecasting is essential for ionospheric monitoring and satellite communications. However, it faces challenges due to limited data and difficulty in modeling long‐term dependencies. This study develops a highly accurate model for 1‐day glob...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003472 |
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| _version_ | 1841536329690644480 |
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| author | Yuhuan Yuan Guozhen Xia Xinmiao Zhang Chen Zhou |
| author_facet | Yuhuan Yuan Guozhen Xia Xinmiao Zhang Chen Zhou |
| author_sort | Yuhuan Yuan |
| collection | DOAJ |
| description | Abstract Accurate 1‐day global total electron content (TEC) forecasting is essential for ionospheric monitoring and satellite communications. However, it faces challenges due to limited data and difficulty in modeling long‐term dependencies. This study develops a highly accurate model for 1‐day global TEC forecasting. We utilized generative TEC data augmentation based on the International Global Navigation Satellite Service (IGS) data set from 1998 to 2017 to enhance the model's prediction ability. Our model takes the TEC sequence of the previous 2 days as input and predicts the global TEC value for each hourly step of the next day. We compared the performance of our model with 1‐day predicted ionospheric products provided by both the Center for Orbit Determination in Europe (C1PG) and Beihang University (B1PG). We proposed a two‐step framework: (a) a time series generative model to produce realistic synthetic TEC data for training, and (b) an auto‐correlation‐based transformer model designed to capture long‐range dependencies in the TEC sequence. Experiments demonstrate that our model significantly improves 1‐day forecast accuracy over prior approaches. On the 2018 benchmark data set, the global root mean squared error (RMSE) of our model is reduced to 1.17 TEC units (TECU), while the RMSE of the C1PG model is 2.07 TECU. Reliability is higher in middle and high latitudes but lower in low latitudes (RMSE < 2.5 TECU), indicating room for improvement. This study highlights the potential of using data augmentation and auto‐correlation‐based transformer models trained on synthetic data to achieve high‐quality 1‐day global TEC forecasting. |
| format | Article |
| id | doaj-art-54be1a1a453e480c9aa7de5e7866d0f1 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-54be1a1a453e480c9aa7de5e7866d0f12025-01-14T16:31:16ZengWileySpace Weather1542-73902023-10-012110n/an/a10.1029/2023SW003472Synthesis‐Style Auto‐Correlation‐Based Transformer: A Learner on Ionospheric TEC Series ForecastingYuhuan Yuan0Guozhen Xia1Xinmiao Zhang2Chen Zhou3Department of Space Physics Wuhan University Wuhan ChinaDepartment of Space Physics Wuhan University Wuhan ChinaDepartment of Space Physics Wuhan University Wuhan ChinaDepartment of Space Physics Wuhan University Wuhan ChinaAbstract Accurate 1‐day global total electron content (TEC) forecasting is essential for ionospheric monitoring and satellite communications. However, it faces challenges due to limited data and difficulty in modeling long‐term dependencies. This study develops a highly accurate model for 1‐day global TEC forecasting. We utilized generative TEC data augmentation based on the International Global Navigation Satellite Service (IGS) data set from 1998 to 2017 to enhance the model's prediction ability. Our model takes the TEC sequence of the previous 2 days as input and predicts the global TEC value for each hourly step of the next day. We compared the performance of our model with 1‐day predicted ionospheric products provided by both the Center for Orbit Determination in Europe (C1PG) and Beihang University (B1PG). We proposed a two‐step framework: (a) a time series generative model to produce realistic synthetic TEC data for training, and (b) an auto‐correlation‐based transformer model designed to capture long‐range dependencies in the TEC sequence. Experiments demonstrate that our model significantly improves 1‐day forecast accuracy over prior approaches. On the 2018 benchmark data set, the global root mean squared error (RMSE) of our model is reduced to 1.17 TEC units (TECU), while the RMSE of the C1PG model is 2.07 TECU. Reliability is higher in middle and high latitudes but lower in low latitudes (RMSE < 2.5 TECU), indicating room for improvement. This study highlights the potential of using data augmentation and auto‐correlation‐based transformer models trained on synthetic data to achieve high‐quality 1‐day global TEC forecasting.https://doi.org/10.1029/2023SW003472ionospheric total electron contentpredictionauto‐correlation‐based transformerdata augmentationvariational autoencoder |
| spellingShingle | Yuhuan Yuan Guozhen Xia Xinmiao Zhang Chen Zhou Synthesis‐Style Auto‐Correlation‐Based Transformer: A Learner on Ionospheric TEC Series Forecasting Space Weather ionospheric total electron content prediction auto‐correlation‐based transformer data augmentation variational autoencoder |
| title | Synthesis‐Style Auto‐Correlation‐Based Transformer: A Learner on Ionospheric TEC Series Forecasting |
| title_full | Synthesis‐Style Auto‐Correlation‐Based Transformer: A Learner on Ionospheric TEC Series Forecasting |
| title_fullStr | Synthesis‐Style Auto‐Correlation‐Based Transformer: A Learner on Ionospheric TEC Series Forecasting |
| title_full_unstemmed | Synthesis‐Style Auto‐Correlation‐Based Transformer: A Learner on Ionospheric TEC Series Forecasting |
| title_short | Synthesis‐Style Auto‐Correlation‐Based Transformer: A Learner on Ionospheric TEC Series Forecasting |
| title_sort | synthesis style auto correlation based transformer a learner on ionospheric tec series forecasting |
| topic | ionospheric total electron content prediction auto‐correlation‐based transformer data augmentation variational autoencoder |
| url | https://doi.org/10.1029/2023SW003472 |
| work_keys_str_mv | AT yuhuanyuan synthesisstyleautocorrelationbasedtransformeralearneronionospherictecseriesforecasting AT guozhenxia synthesisstyleautocorrelationbasedtransformeralearneronionospherictecseriesforecasting AT xinmiaozhang synthesisstyleautocorrelationbasedtransformeralearneronionospherictecseriesforecasting AT chenzhou synthesisstyleautocorrelationbasedtransformeralearneronionospherictecseriesforecasting |